Bibliography
[1]

Jerry B. Abrams and Mark E. Tuckerman. Efficient and Direct Generation of Multidimensional Free Energy Surfaces via Adiabatic Dynamics without Coordinate Transformations. J. Phys. Chem. B, 112(49):15742–15757, DEC 11 2008.

[2]

Dilnoza Amirkulova and Andrew D. White. Recent advances in maximum entropy biasing techniques for molecular dynamics. arXiv preprint arXiv:1902.02252, 2019.

[3]

Alejandro Gil-Ley Andrea Cesari and Giovanni Bussi. Combining simulations and solution experiments as a paradigm for RNA force field refinement. J Chem Theory Comput, 12(12):6192–6200, dec 2016.

[4]

Stefano Angioletti-Uberti, Michele Ceriotti, Peter D. Lee, and Mike W. Finnis. Solid-liquid interface free energy through metadynamics simulations. Phys. Rev. B, 81:125416, 2010.

[5]

V. Babin, C. Roland, and C. Sagui. Adaptively biased molecular dynamics for free energy calculations. J. Chem. Phys., 128:134101, 2008.

[6]

Francis Bach and Eric Moulines. Non-strongly-convex smooth stochastic approximation with convergence rate o(1/n). In C.J.C. Burges, L. Bottou, M. Welling, Z. Ghahramani, and K.Q. Weinberger, editors, Advances in Neural Information Processing Systems 26, pages 773–781. Curran Associates, Inc., Red Hook, NY, 2013.

[7]

Fahimeh Baftizadeh, Pilar Cossio, Fabio Pietrucci, and Alessandro Laio. Protein folding and ligand-enzyme binding from bias-exchange metadynamics simulations. Curr Phys Chem, 2:79–91, 2012.

[8]

A Barducci, G Bussi, and M Parrinello. Well-tempered metadynamics: A smoothly converging and tunable free-energy method. Phys. Rev. Lett., 100(2):020603, Jan 2008.

[9]

Alessandro Barducci, Massimiliano Bonomi, and Michele Parrinello. Metadynamics. Wiley Interdisciplinary Reviews: Computational Molecular Science, 1(5):826–843, 2011.

[10]

C. Bartels and M. Karplus. Probability Distributions for Complex Systems: Adaptive Umbrella Sampling of the Potential Energy. J. Phys. Chem. B, 102(5):865–880, 1998.

[11]

Bernd A. Berg and Thomas Neuhaus. Multicanonical ensemble: A new approach to simulate first-order phase transitions. Phys. Rev. Lett., 68:9–12, Jan 1992.

[12]

R. B. Best, G. Hummer, and W. A. Eaton. Native contacts determine protein folding mechanisms in atomistic simulations. Proc. Natl. Acad. Sci. U.S.A., 110(44):17874–17879, 2013.

[13]

Xevi Biarnés, Albert Ardevol, Antoni Planas, Carme Rovira, Alessandro Laio, and Michele Parrinello. The conformational free energy landscape of β-d-glucopyranose. implications for substrate preactivation in β-glucoside hydrolases. Journal of the American Chemical Society, 129(35):10686–10693, 2007.

[14]

Massimiliano Bonomi and Carlo Camilloni. Integrative structural and dynamical biology with PLUMED-ISDB. Bioinformatics, 33:3999–4000, 2017.

[15]

M. Bonomi and M. Parrinello. Enhanced sampling in the well-tempered ensemble. Phys. Rev. Lett., 104:190601, 2010.

[16]

Massimiliano Bonomi, Davide Branduardi, Giovanni Bussi, Carlo Camilloni, Davide Provasi, Paolo Raiteri, Davide Donadio, Fabrizio Marinelli, Fabio Pietrucci, Ricardo A Broglia, and Michele Parrinello. PLUMED: A portable plugin for free-energy calculations with molecular dynamics. Computer Physics Communications, 180(10):1961–1972, 2009.

[17]

Massimiliano Bonomi, Carlo Camilloni, Andrea Cavalli, and Michele Vendruscolo. Metainference: A Bayesian inference method for heterogeneous systems. Science Advances, 2(1):e1501177, 2016.

[18]

Massimiliano Bonomi, Carlo Camilloni, and Michele Vendruscolo. Metadynamic metainference: Enhanced sampling of the metainference ensemble using metadynamics. Sci. Rep., 6:31232, 2016.

[19]

Wouter Boomsma, Kresten Lindorff-Larsen, and Jesper Ferkinghoff-Borg. Combining Experiments and Simulations Using the Maximum Entropy Principle. PLoS Comput. Biol., 10(2):e1003406, 2014.

[20]

Sandro Bottaro, Francesco Di Palma, and Giovanni Bussi. The role of nucleobase interactions in rna structure and dynamics. Nucleic acids research, 21(42):13306–13314, 2014.

[21]

Davide Branduardi, Francesco Luigi Gervasio, and Michele Parrinello. From A to B in free energy space. J. Chem. Phys., 126(5):054103, Feb 2007.

[22]

D Branduardi, G Bussi, and M PARRINELLO. Metadynamics with adaptive Gaussians. J. Chem. Theory Comput., 8(7):2247–2254, 2012.

[23]

Giovanni Bussi, Francesco Luigi Gervasio, Alessandro Laio, and Michele Parrinello. Free-energy landscape for beta hairpin folding from combined parallel tempering and metadynamics. J. Am. Chem. Soc., 128(41):13435–41, 2006.

[24]

Giovanni Bussi, Davide Branduardi, and others. Free-energy calculations with metadynamics: Theory and practice. Rev. Comput. Chem, 28:1–49, 2015.

[25]

Giovanni Bussi. Hamiltonian replica-exchange in gromacs: a flexible implementation. Mol. Phys., 2013. DOI: 10.1080/00268976.2013.824126.

[26]

Carlo Camilloni and Michele Vendruscolo. Statistical mechanics of the denatured state of a protein using replica-averaged metadynamics. J. Am. Chem. Soc., 136(25):8982–8991, 2014.

[27]

Carlo Camilloni and Michele Vendruscolo. A Tensor-Free Method for the Structural and Dynamical Refinement of Proteins using Residual Dipolar Couplings. J. Phys. Chem. B, 119(3):653–661, 2015.

[28]

Carlo Camilloni and Michele Vendruscolo. Using Pseudocontact Shifts and Residual Dipolar Couplings as Exact NMR Restraints for the Determination of Protein Structural Ensembles. Biochemistry, 54(51):7470–7476, 2015.

[29]

C. Camilloni, R. A. Broglia, and G. Tiana. Hierarchy of folding and unfolding events of protein g, ci2, and acbp from explicit-solvent simulations. J. Chem. Phys., 134:045105, 2011.

[30]

Carlo Camilloni, Paul Robustelli, Alfonso De Simone, Andrea Cavalli, and Michele Vendruscolo. Characterization of the Conformational Equilibrium between the Two Major Substates of RNase A Using NMR Chemical Shifts. J. Am. Chem. Soc., 134(9):3968–3971, 2012.

[31]

Carlo Camilloni, Andrea Cavalli, and Michele Vendruscolo. Assessment of the Use of NMR Chemical Shifts as Replica-Averaged Structural Restraints in Molecular Dynamics Simulations to Characterize the Dynamics of Proteins. J. Phys. Chem. B, 117(6):1838–1843, 2013.

[32]

Carlo Camilloni, Andrea Cavalli, and Michele Vendruscolo. Replica-Averaged Metadynamics. J. Chem. Theory Comput., 9(12):5610–5617, 2013.

[33]

Riccardo Capelli, Guido Tiana, and Carlo Camilloni. An implementation of the maximum-caliber principle by replica-averaged time-resolved restrained simulations. J. Chem. Phys., 148(18):184114, May 2018.

[34]

Andrea Cavalli, Carlo Camilloni, and Michele Vendruscolo. Molecular dynamics simulations with replica-averaged structural restraints generate structural ensembles according to the maximum entropy principle. J. Chem. Phys., 138(9):094112, 2013.

[35]

Haochuan Chen, Haohao Fu, Xueguang Shao, Christophe Chipot, and Wensheng Cai. ELF: An extended-lagrangian free energy calculation module for multiple molecular dynamics engines. Journal of Chemical Information and Modeling, 58:1315–1318, Jun 2018.

[36]

Bingqing Cheng, Gareth A. Tribello, and Michele Ceriotti. Solid-liquid interfacial free energy out of equilibrium. Phys. Rev. B, 92:180102, 2015.

[37]

D t Cremer and JA Pople. General definition of ring puckering coordinates. Journal of the American Chemical Society, 97(6):1354–1358, 1975.

[38]

Richard A Cunha and Giovanni Bussi. Unraveling mg2+–rna binding with atomistic molecular dynamics. RNA, 23(5):628–638, 2017.

[39]

Jeremy Curuksu and Martin Zacharias. Enhanced conformational sampling of nucleic acids by a new hamiltonian replica exchange molecular dynamics approach. The Journal of chemical physics, 130(10):03B610, 2009.

[40]

James F Dama, Michele Parrinello, and Gregory A Voth. Well-tempered metadynamics converges asymptotically. Phys. Rev. Lett., 112(24):240602, 2014.

[41]

Michael Deighan, Massimiliano Bonomi, and Jim Pfaendtner. Efficient simulation of explicitly solvated proteins in the well-tempered ensemble. Journal of Chemical Theory and Computation, 8(7):2189–2192, 2012.

[42]

Grisell Díaz Leines and Bernd Ensing. Path finding on high-dimensional free energy landscapes. Phys. Rev. Lett., 109:020601, 2012.

[43]

Trang N. Do, Paolo Carloni, Gabriele Varani, and Giovanni Bussi. Rna/peptide binding driven by electrostatics—insight from bidirectional pulling simulations. Journal of Chemical Theory and Computation, 9(3):1720–1730, 2013.

[44]

Marco Jacopo Ferrarotti, Sandro Bottaro, Andrea Perez-Villa, and Giovanni Bussi. Accurate multiple time step in biased molecular simulations. J. Chem. Theory Comput., 11(1):139–146, 2015.

[45]

Haohao Fu, Xueguang Shao, Christophe Chipot, and Wensheng Cai. Extended adaptive biasing force algorithm. an on-the-fly implementation for accurate free-energy calculations. Journal of Chemical Theory and Computation, 12(8):3506–3513, aug

[46]

Grégoire A. Gallet and Fabio Pietrucci. Structural cluster analysis of chemical reactions in solution. The Journal of Chemical Physics, 139(7):074101, 2013.

[47]

Federico Giberti, Gareth A. Tribello, and Michele Parrinello. Transient polymorphism in nacl. Journal of Chemical Theory and Computation, 9(2526-2530):null, 2013.

[48]

Federico Giberti, Matteo Salvalaglio, Marco Mazzotti, and Michele Parrinello. Insight into the nucleation of urea crystals from the melt. Chemical Engineering Science, 121:51 – 59, 2015. 2013 Danckwerts Special Issue on Molecular Modelling in Chemical Engineering.

[49]

Alejandro Gil-Ley and Giovanni Bussi. Enhanced conformational sampling using replica exchange with collective-variable tempering. Journal of chemical theory and computation, 11(3):1077–1085, 2015.

[50]

Alejandro Gil-Ley and Giovanni Bussi. Empirical corrections to the amber rna force field with target metadynamics, 2016.

[51]

Daniele Granata, Carlo Camilloni, Michele Vendruscolo, and Alessandro Laio. Characterization of the free-energy landscapes of proteins by NMR-guided metadynamics. Proc. Natl. Acad. Sci. U.S.A., 110(17):6817–6822, 2013.

[52]

H. Grubmüller, B. A. Heymann, and P. Tavan. Science, 271:997–999, 1996.

[53]

Samuel Hanot, Massimiliano Bonomi, Charles H Greenberg, Andrej Sali, Michael Nilges, Michele Vendruscolo, and Riccardo Pellarin. Multi-scale bayesian modeling of cryo-electron microscopy density maps. bioRxiv, page doi: 10.1101/113951, 2017.

[54]

Michael J Hartmann, Yuvraj Singh, Eric Vanden-Eijnden, and Glen M Hocky. Infinite switch simulated tempering in force (fisst). arXiv:1910.14064, 2019.

[55]

Glen M. Hocky, Thomas Dannenhoffer-Lafage, and Gregory A. Voth. Coarse-grained directed simulation. Journal of Chemical Theory and Computation, 13(9):4593–4603, 2017.

[56]

Ladislav Hovan, Federico Comitani, and Francesco L Gervasio. An Optimal Metric for the Path Collective Variables. Journal of Chemical Theory and Computation, 15(1):25–32, 2019.

[57]

Ming Huang, Timothy J Giese, Tai-Sung Lee, and Darrin M York. Improvement of dna and rna sugar pucker profiles from semiempirical quantum methods. Journal of chemical theory and computation, 10(4):1538–1545, 2014.

[58]

M. Iannuzzi, A. Laio, and M. Parrinello. Efficient exploration of reactive potential energy surfaces using car-parrinello molecular dynamics. Phys. Rev. Lett., 90:238302, 2003.

[59]

Michele Invernizzi and Michele Parrinello. Making the best of a bad situation: a multiscale approach to free energy calculation. J. Chem. Theory Comput., 15(4):2187–2194, 2019.

[60]

C. Jarzynski. Nonequilibrium equality for free energy differences. Phys. Rev. Lett., 78:2690–2693, 1997.

[61]

S. K. Kearsley. On the orthogonal transformation used for structural comparison. Acta Cryst. A, 45:208–210, 1989.

[62]

KJ Kohlhoff, Paul Robustelli, Andrea Cavalli, Xavier Salvatella, and Michele Vendruscolo. Fast and accurate predictions of protein NMR chemical shifts from interatomic distances. J. Am. Chem. Soc., 131(39):13894–13895, 2009.

[63]

Alessandrio Laio and Francesco Luigi Gervasio. Metadynamics: a method to simulate rare events and reconstruct the free energy in biophysics, chemistry and material science. Rep. Prog. Phys., 71:126601, 2008.

[64]

A. Laio and M. Parrinello. Escaping free energy minima. Proc. Natl. Acad. Sci. USA, 99:12562–12566, 2002.

[65]

Wolfgang Lechner and Christoph Dellago. Accurate determination of crystal structures based on averaged local bond order parameters. The Journal of Chemical Physics, 129(11):–, 2008.

[66]

Grisell Díaz Leines and Bernd Ensing. Path finding on high-dimensional free energy landscapes. Phys. Rev. Lett., 109:020601, Feb 2012.

[67]

Tony Lelièvre, Mathias Rousset, and Gabriel Stoltz. Computation of free energy profiles with parallel adaptive dynamics. The Journal of Chemical Physics, 126(13):134111, apr 2007.

[68]

Adrien Lesage, Tony Lelièvre, Gabriel Stoltz, and Jérôme Hénin. Smoothed biasing forces yield unbiased free energies with the extended-system adaptive biasing force method. The Journal of Physical Chemistry B, 121(15):3676–3685, dec 2016.

[69]

Thomas Löhr, Alexander Jussupow, and Carlo Camilloni. Metadynamic metainference: Convergence towards force field independent structural ensembles of a disordered peptide. J. Chem. Phys., 146(16):165102–11, 2017.

[70]

L. Maragliano and E. Vanden-Eijnden. A temperature-accelerated method for sampling free energy and determining reaction pathways in rare events simulations. Chem. Phys. Lett., 426:168–175, 2006.

[71]

M. Marchi and P. Ballone. Adiabatic bias molecular dynamics: A method to navigate the conformational space of complex molecular systems. J. Chem. Phys., 110(8):3697–3702, 1999.

[72]

Fabrizio Marinelli and José D Faraldo-Gómez. Ensemble-biased metadynamics: A molecular simulation method to sample experimental distributions. Biophys. J., 108(12):2779–2782, 2015.

[73]

Fabrizio Marinelli, Fabio Pietrucci, Alessandro Laio, and Stefano Piana. A kinetic model of trp-cage folding from multiple biased molecular dynamics simulations. PLoS Comput. Biol., 5(8):e100045, 2009.

[74]

James McCarty, Omar Valsson, Pratyush Tiwary, and Michele Parrinello. Variationally optimized free-energy flooding for rate calculation. Phys. Rev. Lett., 115(7):070601, 2015.

[75]

Cristian Micheletti, Alessandro Laio, and Michele Parrinello. Reconstructing the density of states by history-dependent metadynamics. Phys. Rev. Lett., 92(17):170601, April 2004.

[76]

T. Morishita, S. G. Itoh, H. Okumura, and M. Mikami. Free-energy calculation via mean-force dynamics using a logarithmic energy landscape. Physical Review E, 85:066702, 2012.

[77]

T. Morishita, Y Yonezawa, and A. M. Ito. Free energy reconstruction from logarithmic mean-force dynamics using multiple nonequilibrium trajectories. Journal of Chemical Theory and Computation, 13:3106, 2017.

[78]

T. Morishita, T Nakamura, W Shinoda, and A. M. Ito. Isokinetic approach in logarithmic mean-force dynamics for on-the-fly free energy reconstruction. Chemical Physics Letter, 706:633, 2018.

[79]

Marco Nava, Ferruccio Palazzesi, Claudio Perego, and Michele Parrinello. Dimer metadynamics. Journal of Chemical Theory and Computation, 13(2):425–430, 2017.

[80]

Hisashi Okumura and Yuko Okamoto. Molecular dynamics simulations in the multibaric–multithermal ensemble. Chemical Physics Letters, 391(4):248 – 253, 2004.

[81]

Andrea Pérez-Villa, Maria Darvas, and Giovanni Bussi. Atp dependent ns3 helicase interaction with rna: insights from molecular simulations. Nucleic Acids Research, 43(18):8725, 2015.

[82]

Jim Pfaendtner and Massimiliano Bonomi. Efficient sampling of high-dimensional free-energy landscapes with parallel bias metadynamics. Journal of Chemical Theory and Computation, 11(11):5062–5067, 2015.

[83]

Pablo M. Piaggi and Michele Parrinello. Multithermal-multibaric molecular simulations from a variational principle. Phys. Rev. Lett., 122:050601, Feb 2019.

[84]

Pablo M Piaggi and Michele Parrinello. Calculation of phase diagrams in the multithermal-multibaric ensemble. The Journal of chemical physics, 150(24):244119, 2019.

[85]

Stefano Piana and Alessandro Laio. A bias-exchange approach to protein folding. J. Phys. Chem. B, 111(17):4553–9, 2007.

[86]

F. Pietrucci and A. Laio. A collective variable for the efficient exploration of protein beta-structures with metadynamics: application to sh3 and gb1. J. Chem. Theory Comput., 5(9):2197–2201, 2009.

[87]

S. Pipolo, M. Salanne, G. Ferlat, S. Klotz, A. M. Saitta, and F. Pietrucci. Navigating at will on the water phase diagram. Phys. Rev. Lett., 119:245701, Dec 2017.

[88]

D. Provasi and M. Filizola. Putative active states of a prototypic g-protein-coupled receptor from biased molecular dynamics. Biophys. J., 98:2347––2355, 2010.

[89]

P. Raiteri, A. Laio, F.L. Gervasio, C. Micheletti, and M. Parrinello. Efficient reconstruction of complex free energy landscapes by multiple walkers metadynamics. J. Phys. Chem. B, 110:3533–3539, 2006.

[90]

Paul Robustelli, Kai Kohlhoff, Andrea Cavalli, and Michele Vendruscolo. Using NMR chemical shifts as structural restraints in molecular dynamics simulations of proteins. Structure, 18(8):923–933, 2010.

[91]

Gabriele C. Sosso, Gareth A. Tribello, Andrea Zen, Philipp Pedevilla, and Angelos Michaelides. Ice formation on kaolinite: Insights from molecular dynamics simulations. The Journal of Chemical Physics, 145(21):211927, 2016.

[92]

Vojtech Spiwok and Blanka Králová. Metadynamics in the conformational space nonlinearly dimensionally reduced by Isomap. Journal of Chemical Physics, 135(22):224504, 2011.

[93]

Vojtech Spiwok, Petra Lipovová, and Blanka Králová. Metadynamics in essential coordinates: free energy simulation of conformational changes. The journal of physical chemistry B, 111(12):3073–6, Mar 2007.

[94]

Yuji Sugita and Yuko Okamoto. Replica-exchange molecular dynamics method for protein folding. Chem. Phys. Lett., 314(1–2):141–151, November 1999.

[95]

L Sutto, M D Abramo, and F L Gervasio. Comparing the efficiency of biased and unbiased molecular dynamics in reconstructing the free energy landscape of met-enkephalin. J. Chem. Theory Comput., 6(12):3640–3646, 2010.

[96]

Ludovico Sutto, Simone Marsili, and Francesco Luigi Gervasio. New advances in metadynamics. Wiley Interdisciplinary Reviews: Computational Molecular Science, 2(5):771–779, 2012.

[97]

Pratyush Tiwary and Michele Parrinello. From metadynamics to dynamics. Phys. Rev. Lett., 111:230602, 2013.

[98]

Pratyush Tiwary and Michele Parrinello. A time-independent free energy estimator for metadynamics. The Journal of Physical Chemistry B, 119(3):736–742, Jan 2015.

[99]

Pratyush Tiwary and Michele Parrinello. A time-independent free energy estimator for metadynamics. The Journal of Physical Chemistry B, 119(3):736–742, 2015. PMID: 25046020.

[100]

G.M. Torrie and J.P. Valleau. Nonphysical sampling distributions in monte carlo free energy estimation: Umbrella sampling. J. Comput. Phys., 23:187–199, 1977.

[101]

Gareth A. Tribello, Jérôme Cuny, Hagai Eshet, and Michele Parrinello. Exploring the free energy surfaces of clusters using reconnaissance metadynamics. J. Chem. Phys., 135(11):114109, 2011.

[102]

Gareth A. Tribello, Massimiliano Bonomi, Davide Branduardi, Carlo Camilloni, and Giovanni Bussi. Plumed 2: New feathers for an old bird. Comput. Phys. Commun., 185(2):604–613, 2014.

[103]

Gareth A. Tribello, Federico Giberti, Gabriele C. Sosso, Matteo Salvalaglio, and Michele Parrinello. Analyzing and driving cluster formation in atomistic simulations. Journal of Chemical Theory and Computation, 13(3):1317–1327, 2017.

[104]

Omar Valsson and Michele Parrinello. Variational approach to enhanced sampling and free energy calculations. Phys. Rev. Lett., 113(9):090601, 2014.

[105]

Omar Valsson and Michele Parrinello. Well-Tempered Variational Approach to Enhanced Sampling. J. Chem. Theory Comput., 11(5):1996–2002, 2015.

[106]

Ji v rí Vym v etal and Ji v rí Vondrá v sek. Gyration- and Inertia-Tensor-Based Collective Coordinates for Metadynamics. Application on the Conformational Behavior of Polyalanine Peptides and Trp-Cage Folding. J. Phys. Chem. A, page 110930112611005, 2011.

[107]

F. G. Wang and D. P. Landau. Efficient, multiple-range random walk algorithm to calculate the density of states. Phys. Rev. Lett., 86:2050–2053, 2001.

[108]

Lingle Wang, Richard A Friesner, and BJ Berne. Replica exchange with solute scaling: A more efficient version of replica exchange with solute tempering (rest2). The Journal of Physical Chemistry B, 115(30):9431–9438, 2011.

[109]

Yong Wang, Omar Valsson, Pratyush Tiwary, Michele Parrinello, and Kresten Lindorff-Larsen. Frequency adaptive metadynamics for the calculation of rare-event kinetics. The Journal of Chemical Physics, 149(7):072309, Aug 2018.

[110]

Andrew D White and Gregory A Voth. An Efficient and Minimal Method to Bias Molecular Simulations with Experimental Data. Journal of Chemical Theory and Computation, 10:3023–3030, 2014.

[111]

Andrew D White, James F Dama, and Gregory A Voth. Designing free energy surfaces that match experimental data with metadynamics. J. Chem. Theory Comput., 11(6):2451–2460, 2015.

[112]

Adam P. Willard and David Chandler. Instantaneous liquid interfaces. The Journal of Physical Chemistry B, 114(5):1954–1958, 2010.

[113]

Tanfeng Zhao, Haohao Fu, Tony Lelièvre, Xueguang Shao, Christophe Chipot, and Wensheng Cai. The extended generalized adaptive biasing force algorithm for multidimensional free-energy calculations. Journal of Chemical Theory and Computation, 13(4):1566–1576, 2017.

[114]

Lianqing Zheng and Wei Yang. Practically efficient and robust free energy calculations: Double-integration orthogonal space tempering. Journal of Chemical Theory and Computation, 8(3):810–823, mar 2012.